Toward robust surrogate components of land surface states for hybrid earth system models
0The land surface, including the biosphere, is an important component of earth system models (ESMs) and weather forecasting systems: It provides terrestrial boundary conditions and surface fluxes of energy, water and carbon over land inform parameters of atmospheric processes. At this interface, ESMs profit from hybridasation where the numerical surface parameterisations are substituted with differentiable emulators. This can strongly reduce runtimes of forward and backward procedures, such as ensemble generation for uncertainty quantification, or deriving adjoint models in data assimilation. Neural networks as powerful function approximators seem suitable candidates, yet they tend to violate physical contraints and consequently can cause instabilities in numerical model predictions. This talk will overview model development parts of my PhD in the environmental sciences and synthesise aspects of constraining neural networks from a land surface forecasting perspective. We explore different strategies for enforcing physical constraints at the example of an self-standing simple process-model for carbon turnover. Further, we compare the potential of different machine learning approaches for land surface emulation with ECMWF‘s land surface scheme ECLand. Finally we outline how a combining approaches targets a robust surrogate model with explainability at best.
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